Multi-Graph Fusion Networks for Urban Region Embedding (IJCAI-22)

This is the implementation of Multi-Graph Fusion Networks for Urban Region Embedding (MGFN) in the following paper:

Shangbin Wu#, Xu Yan#, Xiaoliang Fan*, Shirui Pan, Shichao Zhu, Chuanpan Zheng, Ming Cheng, Cheng Wang, Multi-Graph Fusion Networks for Urban Region Embedding, International Joint Conference on Artificial Intelligence (IJCAI-22), July 23-29, 2022 Messe Wien, Vienna, Austria.[Acceptance rate=15%]

Multi-Graph Fusion Networks for Urban Region Embedding (MGFN, https://arxiv.org/pdf/2201.09760.pdf) was accepted by IJCAI-2022.

Table of Contents

• Data
• Requirements
• QuickStart
• These Features are Coming Soon
• Citation
• Contacts
• Reference

Data

Here we provide the processed data. And the Raw Data can be found in: NYC OpenData: https://opendata.cityofnewyork.us/.
We followed the settings in [Zhang et al., 2020] that apply taxi trip data as human mobility data and take the crime count, check-in count, land usage type as prediction tasks, respectively.

Requirements

Python 3.7.9,
pytorch 1.5.1,
numpy 1.19.2,
pandas 0.25.3,
sklearn 0.24.1

QuickStart

run the command below to train the MGFN:

python mgfn.py

These Features are Coming Soon

The code about...

• Visualization of mobility pattern
• Generalization ability analysis
• Data preprocessing

Citation

Please cite our paper in your publications if this code helps your research.

@article{wu2022multi_graph,
  title={Multi-Graph Fusion Networks for Urban Region Embedding},
  author={Wu, Shangbin and Yan, Xu and Fan, Xiaoliang and Pan, Shirui and Zhu, Shichao and Zheng, Chuanpan and Cheng, Ming and Wang, Cheng},
  journal={arXiv preprint arXiv:2201.09760},
  year={2022}
}

Contacts

Shangbin Wu, [email protected]

Xiaoliang Fan (corresponding author), [email protected], https://fanxlxmu.github.io

Reference